NEXUS: Neural Energy Fields for Physically Consistent Contact-Rich 3D Object Dynamics

2026-06-12 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

NEXUS, a neural energy-field framework, addresses the challenge of generating physics-grounded videos with controllable, physically consistent 3D object dynamics in contact-rich scenes. Unlike trajectory-based methods that struggle with composing conservative and non-conservative effects, NEXUS represents objects as structural graphs and constructs dynamic contact graphs. Inspired by Hamiltonian Neural Networks, it formulates motion using scalar energy and dissipation terms, rather than directly predicting states. Conservative effects like gravity and elastic deformation are additive energy terms, while non-conservative effects such as damping are modeled with learned Rayleigh-style dissipation. Forces are derived by differentiating these functions and rolled out with a multi-substep semi-implicit integrator. NEXUS significantly improves long-horizon accuracy over baselines in controlled trajectory benchmarks and enhances physical plausibility in contact-rich video generation while maintaining visual quality.

Key takeaway

For Computer Vision Engineers developing physically consistent 3D simulations or video generation, NEXUS offers a robust framework for handling complex contact dynamics. Your projects requiring high physical plausibility and long-horizon accuracy in contact-rich scenes can benefit significantly. Consider integrating neural energy fields to model both conservative and non-conservative effects, enhancing the realism and stability of your dynamic object interactions.

Key insights

NEXUS models 3D object dynamics using neural energy fields for physically consistent contact-rich interactions.

Principles

• Motion derived from scalar energy and dissipation.
• Conservative effects are additive energy terms.
• Non-conservative effects are learned dissipation.

Method

Objects are structural graphs; dynamic contact graphs are built. Forces are derived by differentiating energy and dissipation functions. Motion is rolled out with a multi-substep semi-implicit integrator.

In practice

• Generate physically plausible contact-rich videos.
• Improve long-horizon accuracy in dynamics simulations.
• Compose conservative and non-conservative dynamics.

Topics

• Neural Energy Fields
• 3D Object Dynamics
• Contact Simulation
• Video Generation
• Hamiltonian Neural Networks
• Physics-based Animation

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.